Drake: An Efficient Executive for Temporal Plans with Choice

TL;DR

Drake is a novel dynamic executive for temporal plans with choices that achieves low latency and compact storage by leveraging labels and environments, significantly reducing memory usage while maintaining efficiency.

Contribution

It introduces a compact, low-latency approach for executing temporal plans with choices using a new labeling scheme inspired by ATMS, improving over prior methods.

Findings

Reduces storage size by over 500 times for large problems.

Maintains low latency despite reduced memory requirements.

Outperforms prior work in efficiency and scalability.

Abstract

This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynamically dispatching Simple Temporal Networks, and further research enriched the expressiveness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce significant storage or latency requirements to make flexible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumption-based Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drakes performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices.